Papers
15
Total Citations
742
H-Index
12
About
Shan Lakhmani is a prominent human factors researcher specializing in human-autonomy teaming, agent transparency, and trust in human-robot interaction. Her work has fundamentally advanced our understanding of how humans collaborate effectively with autonomous systems and artificial intelligence agents in complex operational environments. Lakhmani's most influential contribution is her research on the Situation Awareness-based Agent Transparency (SAT) model, which provides a theoretical and practical framework for designing interfaces that help humans maintain appropriate understanding of and calibrated trust in autonomous agents. Her 2018 paper on SAT and human-autonomy teaming effectiveness has garnered 327 citations, establishing her as a leading voice in the field. Building on this foundation, she has systematically explored how transparency interacts with agent reliability, demonstrating that transparency information meaningfully shapes operator confidence and perceived trustworthiness even under degraded conditions. Her applied work includes designing interfaces for military robotic systems such as the Autonomous Squad Member, translating theoretical models into real-world human-robot teaming scenarios. More recently, her 2022 toolkit for measuring trust in human-autonomy teams addresses critical gaps in assessment methodology. Across her body of work, Lakhmani's research equips designers, engineers, and policymakers with actionable strategies for building safer, more effective human-machine partnerships.
Research Focus
Key Achievements
Top Papers
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- 4Effects of Agent Transparency on Operator Trust46 citations · 2015
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- 7Human-Autonomy Teaming and Agent Transparency30 citations · 2016
- 8Displaying Information to Support Transparency for Autonomous Platforms24 citations · 2016
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